




'''simple docstring'''


from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests
def 						snake_case_       (UpperCamelCase			:    str = "isbn/0140328726" ):





							'''simple docstring'''


							_a      	=							olid.strip().strip('''/''' )  # Remove leading/trailing whitespace & slashes
							if new_olid.count('''/''' ) != 1:
														_a      	=							f'{olid} is not a valid Open Library olid'
														raise ValueError(UpperCamelCase )
							return requests.get(f'https://openlibrary.org/{new_olid}.json' ).json()
def 						snake_case_       (UpperCamelCase			:    dict ):





							'''simple docstring'''


							_a      	=							{
							    '''title''': '''Title''',
							    '''publish_date''': '''Publish date''',
							    '''authors''': '''Authors''',
							    '''number_of_pages''': '''Number of pages:''',
							    '''first_sentence''': '''First sentence''',
							    '''isbn_10''': '''ISBN (10)''',
							    '''isbn_13''': '''ISBN (13)''',
							}
							_a      	=							{better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
							_a      	=							[
							    get_openlibrary_data(author['''key'''] )['''name'''] for author in data['''Authors''']
							]
							_a      	=							data['''First sentence''']['''value''']
							for key, value in data.items():
														if isinstance(UpperCamelCase					,		UpperCamelCase ):
																					_a      	=							''', '''.join(UpperCamelCase )
							return data


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				while True:
								_snake_case : str  				=						input('\nEnter the ISBN code to search (or \'quit\' to stop): ').strip()
								if isbn.lower() in ("", "q", "quit", "exit", "stop"):
												break

								if len(isbn) not in (10, 13) or not isbn.isdigit():
												print(F'''Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.''')
												continue

								print(F'''\nSearching Open Library for ISBN: {isbn}...\n''')

								try:
												_snake_case : str  				=						summarize_book(get_openlibrary_data(F'''isbn/{isbn}'''))
												print('\n'.join(F'''{key}: {value}''' for key, value in book_summary.items()))
								except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
												print(F'''Sorry, there are no results for ISBN: {isbn}.''')






'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:   List[str]					=		logging.get_logger(__name__)

lowerCAmelCase:   Union[str, Any]					=		{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """lilt"""







    def __init__( self					:    str		,     __snake_case					:    Dict=3_05_22		,     __snake_case					:    int=7_68		,     __snake_case					:    Union[str, Any]=12		,     __snake_case					:    Optional[Any]=12		,     __snake_case					:    Dict=30_72		,     __snake_case					:    Tuple="gelu"		,     __snake_case					:    Dict=0.1		,     __snake_case					:    List[Any]=0.1		,     __snake_case					:    List[str]=5_12		,     __snake_case					:    List[str]=2		,     __snake_case					:    str=0.02		,     __snake_case					:    Dict=1e-1_2		,     __snake_case					:    int=0		,     __snake_case					:    Any="absolute"		,     __snake_case					:    Tuple=None		,     __snake_case					:    Tuple=4		,     __snake_case					:    List[str]=10_24		,     **__snake_case					:    Union[str, Any]		,     ):
      super().__init__(pad_token_id=__snake_case		,     **__snake_case      )

      a		:   str	  =	vocab_size
      a		:   Optional[Any]	  =	hidden_size
      a		:   Union[str, Any]	  =	num_hidden_layers
      a		:   Tuple	  =	num_attention_heads
      a		:   Optional[Any]	  =	hidden_act
      a		:   Union[str, Any]	  =	intermediate_size
      a		:   Optional[Any]	  =	hidden_dropout_prob
      a		:   Tuple	  =	attention_probs_dropout_prob
      a		:   str	  =	max_position_embeddings
      a		:   Any	  =	type_vocab_size
      a		:   Union[str, Any]	  =	initializer_range
      a		:   int	  =	layer_norm_eps
      a		:   Any	  =	position_embedding_type
      a		:   Any	  =	classifier_dropout
      a		:   str	  =	channel_shrink_ratio
      a		:   Optional[Any]	  =	max_ad_position_embeddings